Developing improved strains for industrial bioconversions can be achieved through different approaches. When the pathways or networks of interest are simple and have been characterized, the responsible genes can be manipulated accordingly. On the other hand, when a trait of interest is poorly understood and when high throughput screening methods are available, random approaches can be used (30). This is especially useful for complex phenotypes that result from simultaneous action of several genes and for which detailed mechanistic information may be lacking. Stresses encountered in industrial fermentations, such as high temperature, acidity, and osmotic pressure, commonly elicit this type of complex responses (29). Therefore, obtaining robust biocatalysts has been traditionally done through serial rounds of mutagenesis and selection. More recently, gene shuffling has improved on asexual breeding through multi-parental mating of whole cells (25, 32). One limitation of these approaches is that they create untransferable and intractable changes at the genomic level.
Even when the loci involved in a complex response are known, it is unclear what modifications to implement for improving it, as the phenotypic response is connected to the genotype indirectly through the transcriptome and proteome (10). Many efforts in whole-cell engineering have recognized the more direct mapping between transcriptome and phenotype and have tried to manipulate the transcript profile directly (1, 2, 5, 22-24). Global transcription machinery engineering (gTME) has been used successfully to introduce multilocus responses at the transcriptomic level that are transferable between strains. Random mutagenesis of the TATA-binding protein of Saccharomyces cerevisiae has resulted in mutants with increased ethanol tolerance and productivity (1). By applying the same concept to the principal sigma factor of Escherichia coli strains with improved resistance to ethanol and SDS, and with increased lycopene accumulation have been produced (2).